构建了更贴近真实工厂的工业异常检测数据集，支持多类缺陷识别。
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection Dataset
- 基于真实金属零件构建多结构、多外观的异常数据集
- 含8580张图像，缺陷细微且贴近基底材料特征
- 适用于评估真实场景下多类无监督异常检测方法
多类无监督异常检测算法因部署成本低、训练效率高而受到关注。然而，现有工业异常检测数据集存在局限：包含多个难以共线生产的类别，未覆盖多样结构与外观，缺陷也不符合真实特征。为此，我们提出异构同类型工业异常检测（HSS-IAD）数据集，包含8,580张金属类工业部件图像及精确异常标注。这些部件在结构和外观上具有多样性，缺陷细微且接近基底材料。同时提供前景图像用于合成异常生成。我们在该数据集上对主流IAD方法进行了多类与分类独立设置下的评估，验证其能有效弥合现有数据集与真实工厂环境之间的差距。数据集已开源：https://github.com/Qiqigeww/HSS-IAD-Dataset。
原文摘要 · Abstract (English)
Multi-class Unsupervised Anomaly Detection algorithms (MUAD) are receiving increasing attention due to their relatively low deployment costs and improved training efficiency. However, the real-world effectiveness of MUAD methods is questioned due to limitations in current Industrial Anomaly Detection (IAD) datasets. These datasets contain numerous classes that are unlikely to be produced by the same factory and fail to cover multiple structures or appearances. Additionally, the defects do not reflect real-world characteristics. Therefore, we introduce the Heterogeneous Same-Sort Industrial Anomaly Detection (HSS-IAD) dataset, which contains 8,580 images of metallic-like industrial parts and precise anomaly annotations. These parts exhibit variations in structure and appearance, with subtle defects that closely resemble the base materials. We also provide foreground images for synthetic anomaly generation. Finally, we evaluate popular IAD methods on this dataset under multi-class and class-separated settings, demonstrating its potential to bridge the gap between existing datasets and real factory conditions. The dataset is available at https://github.com/Qiqigeww/HSS-IAD-Dataset.
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